Robust single-shot 3D fluorescence imaging in scattering media with a simulator-trained neural network.

Imaging through scattering is a pervasive and difficult problem in many biological applications. The high background and the exponentially attenuated target signals due to scattering fundamentally limits the imaging depth of fluorescence microscopy. Light-field systems are favorable for high-speed volumetric imaging, but the 2D-to-3D reconstruction is fundamentally ill-posed, and scattering exacerbates the condition of the inverse problem. Here, we develop a scattering simulator that models low-contrast target signals buried in heterogeneous strong background. We then train a deep neural network solely on synthetic data to descatter and reconstruct a 3D volume from a single-shot light-field measurement with low signal-to-background ratio (SBR). We apply this network to our previously developed computational miniature mesoscope and demonstrate the robustness of our deep learning algorithm on scattering phantoms with different scattering conditions. The network can robustly reconstruct emitters in 3D with a 2D measurement of SBR as low as 1.05 and as deep as a scattering length. We analyze fundamental tradeoffs based on network design factors and out-of-distribution data that affect the deep learning model's generalizability to real experimental data. Broadly, we believe that our simulator-based deep learning approach can be applied to a wide range of imaging through scattering techniques where experimental paired training data is lacking.

Targeted micro-fiber arrays for measuring and manipulating localized multi-scale neural dynamics over large, deep brain volumes during behavior.

Neural population dynamics relevant to behavior vary over multiple spatial and temporal scales across three-dimensional volumes. Current optical approaches lack the spatial coverage and resolution necessary to measure and manipulate naturally occurring patterns of large-scale, distributed dynamics within and across deep brain regions such as the striatum. We designed a new micro-fiber array approach capable of chronically measuring and optogenetically manipulating local dynamics across over 100 targeted locations simultaneously in head-fixed and freely moving mice, enabling the investigation of cell-type- and neurotransmitter-specific signals over arbitrary 3D volumes at a spatial resolution and coverage previously inaccessible. We applied this method to resolve rapid dopamine release dynamics across the striatum, revealing distinct, modality-specific spatiotemporal patterns in response to salient sensory stimuli extending over millimeters of tissue. Targeted optogenetics enabled flexible control of neural signaling on multiple spatial scales, better matching endogenous signaling patterns, and the spatial localization of behavioral function across large circuits.

Forest fire surveillance systems: A review of deep learning methods.

This review aims to critically examine the existing state-of-the-art forest fire detection systems that are based on deep learning methods. In general, forest fire incidences bring significant negative impact to the economy, environment, and society. One of the crucial mitigation actions that needs to be readied is an effective forest fire detection system that are able to automatically notify the relevant parties on the incidence of forest fire as early as possible. This review paper has examined in details 37 research articles that have implemented deep learning (DL) model for forest fire detection, which were published between January 2018 and 2023. In this paper, in depth analysis has been performed to identify the quantity and type of data that includes images and video datasets, as well as data augmentation methods and the deep model architecture. This paper is structured into five subsections, each of which focuses on a specific application of deep learning (DL) in the context of forest fire detection. These subsections include 1) classification, 2) detection, 3) detection and classification, 4) segmentation, and 5) segmentation and classification. To compare the model's performance, the methods were evaluated using comprehensive metrics like accuracy, mean average precision (mAP), F1-Score, mean pixel accuracy (MPA), etc. From the findings, of the usage of DL models for forest fire surveillance systems have yielded favourable outcomes, whereby the majority of studies managed to achieve accuracy rates that exceeds 90%. To further enhance the efficacy of these models, future research can explore the optimal fine-tuning of the hyper-parameters, integrate various satellite data, implement generative data augmentation techniques, and refine the DL model architecture. In conclusion, this paper highlights the potential of deep learning methods in enhancing forest fire detection that is crucial for forest fire management and mitigation.

Targeted micro-fiber arrays for measuring and manipulating localized multi-scale neural dynamics over large, deep brain volumes during behavior.

Neural population dynamics relevant for behavior vary over multiple spatial and temporal scales across 3-dimensional volumes. Current optical approaches lack the spatial coverage and resolution necessary to measure and manipulate naturally occurring patterns of large-scale, distributed dynamics within and across deep brain regions such as the striatum. We designed a new micro-fiber array and imaging approach capable of chronically measuring and optogenetically manipulating local dynamics across over 100 targeted locations simultaneously in head-fixed and freely moving mice. We developed a semi-automated micro-CT based strategy to precisely localize positions of each optical fiber. This highly-customizable approach enables investigation of multi-scale spatial and temporal patterns of cell-type and neurotransmitter specific signals over arbitrary 3-D volumes at a spatial resolution and coverage previously inaccessible. We applied this method to resolve rapid dopamine release dynamics across the striatum volume which revealed distinct, modality specific spatiotemporal patterns in response to salient sensory stimuli extending over millimeters of tissue. Targeted optogenetics through our fiber arrays enabled flexible control of neural signaling on multiple spatial scales, better matching endogenous signaling patterns, and spatial localization of behavioral function across large circuits.

Pupil engineering for extended depth-of-field imaging in a fluorescence miniscope.

Significance: Fluorescence head-mounted microscopes, i.e., miniscopes, have emerged as powerful tools to analyze in-vivo neural populations but exhibit a limited depth-of-field (DoF) due to the use of high numerical aperture (NA) gradient refractive index (GRIN) objective lenses. Aim: We present extended depth-of-field (EDoF) miniscope, which integrates an optimized thin and lightweight binary diffractive optical element (DOE) onto the GRIN lens of a miniscope to extend the DoF by 2.8× between twin foci in fixed scattering samples. Approach: We use a genetic algorithm that considers the GRIN lens' aberration and intensity loss from scattering in a Fourier optics-forward model to optimize a DOE and manufacture the DOE through single-step photolithography. We integrate the DOE into EDoF-Miniscope with a lateral accuracy of 70 µm to produce high-contrast signals without compromising the speed, spatial resolution, size, or weight. Results: We characterize the performance of EDoF-Miniscope across 5- and 10-µm fluorescent beads embedded in scattering phantoms and demonstrate that EDoF-Miniscope facilitates deeper interrogations of neuronal populations in a 100-µm-thick mouse brain sample and vessels in a whole mouse brain sample. Conclusions: Built from off-the-shelf components and augmented by a customizable DOE, we expect that this low-cost EDoF-Miniscope may find utility in a wide range of neural recording applications.

Robust single-shot 3D fluorescence imaging in scattering media with a simulator-trained neural network.

Imaging through scattering is a pervasive and difficult problem in many biological applications. The high background and the exponentially attenuated target signals due to scattering fundamentally limits the imaging depth of fluorescence microscopy. Light-field systems are favorable for high-speed volumetric imaging, but the 2D-to-3D reconstruction is fundamentally ill-posed, and scattering exacerbates the condition of the inverse problem. Here, we develop a scattering simulator that models low-contrast target signals buried in heterogeneous strong background. We then train a deep neural network solely on synthetic data to descatter and reconstruct a 3D volume from a single-shot light-field measurement with low signal-to-background ratio (SBR). We apply this network to our previously developed Computational Miniature Mesoscope and demonstrate the robustness of our deep learning algorithm on scattering phantoms with different scattering conditions. The network can robustly reconstruct emitters in 3D with a 2D measurement of SBR as low as 1.05 and as deep as a scattering length. We analyze fundamental tradeoffs based on network design factors and out-of-distribution data that affect the deep learning model's generalizability to real experimental data. Broadly, we believe that our simulator-based deep learning approach can be applied to a wide range of imaging through scattering techniques where experimental paired training data is lacking.

High-speed low-light in vivo two-photon voltage imaging of large neuronal populations.

Monitoring spiking activity across large neuronal populations at behaviorally relevant timescales is critical for understanding neural circuit function. Unlike calcium imaging, voltage imaging requires kilohertz sampling rates that reduce fluorescence detection to near shot-noise levels. High-photon flux excitation can overcome photon-limited shot noise, but photobleaching and photodamage restrict the number and duration of simultaneously imaged neurons. We investigated an alternative approach aimed at low two-photon flux, which is voltage imaging below the shot-noise limit. This framework involved developing positive-going voltage indicators with improved spike detection (SpikeyGi and SpikeyGi2); a two-photon microscope ('SMURF') for kilohertz frame rate imaging across a 0.4 mm × 0.4 mm field of view; and a self-supervised denoising algorithm (DeepVID) for inferring fluorescence from shot-noise-limited signals. Through these combined advances, we achieved simultaneous high-speed deep-tissue imaging of more than 100 densely labeled neurons over 1 hour in awake behaving mice. This demonstrates a scalable approach for voltage imaging across increasing neuronal populations.

Enhanced model for leadership development for trainees and early career health professionals: insights from a national survey of UK clinical scientists.

INTRODUCTION: The importance of shared or distributed leadership in healthcare is recognised; however, trainees, early career professionals and others for whom the exercise of leadership is a recent development report being underprepared for leadership roles. Trainee clinical scientists exemplify such groups, being both early in their career and in a profession for which clinical leadership is less well established. Their insights can inform understanding of appropriate forms of leadership development for health professionals. METHODS: We explored perceptions of leadership and its development for trainee clinical scientists on the UK preregistration Scientist Training Programme through semi-structured interviews with trainees, training officers, academic educators and lead healthcare scientists; and through an online questionnaire based on the UK multiprofessional Clinical Leadership Competency Framework (CLCF). Responses were analysed statistically or thematically as appropriate. RESULTS: Forty interviews were undertaken and 267 valid questionnaire responses received. Stakeholders recognised clinical expertise as integral to leadership; otherwise their perceptions aligned with CLCF domains and 'shared leadership' philosophy. They consider learning by 'doing' real tasks (leadership activities) key to competency acquisition, with leadership education (eg, observation and theory) complementing these. Workplace affordances, such as quality of departmental leadership, training officer engagement and degree of patient contact affect trainees' ability to undertake leadership activities. CONCLUSIONS: From our research, we have developed an enhanced model for leadership development for trainee and early career clinical scientists that may have wider applicability to other health professions and groups not traditionally associated with clinical leadership. To foster their leadership, we argue that improving workplace affordances is more important than improving leadership education.

The Bruce effect: Representational stability and memory formation in the accessory olfactory bulb of the female mouse.

In the Bruce effect, a mated female mouse becomes resistant to the pregnancy-blocking effect of the stud. Various lines of evidence suggest that this form of behavioral imprinting results from reduced sensitivity of the female's accessory olfactory bulb (AOB) to the stud's chemosignals. However, the AOB's combinatorial code implies that diminishing responses to one individual will distort representations of other stimuli. Here, we record extracellular responses of AOB neurons in mated and unmated female mice while presenting urine stimuli from the stud and from other sources. We find that, while initial sensory responses in the AOB (within a timescale required to guide social interactions) remain stable, responses to extended stimulation (as required for eliciting the pregnancy block) display selective attenuation of stud-responsive neurons. Such temporal disassociation could allow attenuation of slow-acting endocrine processes in a stimulus-specific manner without compromising ongoing representations that guide behavior.

Enhancing trainee clinical scientists' self-regulated learning in the workplace.

BACKGROUND: Trainee health professionals must be competent self-regulated learners, particularly when learning in busy, unpredictable clinical settings. Whilst research indicates self-regulated learning (SRL) is influenced both by learners' individual actions and their interactions with others, how these combine to foster SRL requires further exploration. We have used Zimmerman's learner-focused SRL model and the situative perspective of communities of practice (CoPs) to investigate how UK trainee clinical scientists regulate their learning. Our aims were to develop a holistic understanding of SRL in the clinical workplace incorporating both individual and social aspects and to suggest ways of maximising learning for trainee clinical scientists and other health professionals. METHODS: Semi-structured interviews were conducted with 13 trainees on the Scientist Training Programme. Transcripts were analysed both inductively and deductively (abductively) using Zimmerman's model and CoPs to explore how trainees regulate their learning. RESULTS: Thematic analysis yielded four themes: approach to learning, engagement and execution of tasks in practice; self-reflection and reaction; and autonomy and role construction. Themes linked concepts from Zimmerman's model and CoPs, as illustrated by our trainee-workplace congruence model. Our model suggests optimal conditions for SRL, and we highlight the importance of trainers in supporting trainee development. CONCLUSIONS: Our trainee-workplace congruence model links concepts from Zimmerman's model and CoPs to provide a framework for understanding how trainee clinical scientists regulate their learning and navigate its social aspects. Whilst trainees must take responsibility for their learning, trainers can facilitate SRL through attention to trainee-workplace 'fit' and encouraging trainee participation in communities of practice.

Institutional influences on the supervision of GP trainees: a documentary analysis.

INTRODUCTION: The supervisory relationship is a key source of support for postgraduate GP trainees in the United Kingdom. This article focuses on the institutional influences on GP supervision through an analysis of training documentation. METHODS: Training documents were identified through a search of key sources of institutional influence: General Medical Council, Royal College of General Practitioners, Health Education West Midlands and a local university's supervisor-training material. Searches were run from September 2016 until February 2019, and 60 documents identified. Content analysis was undertaken, and documents were considered based on audience, context, language and purpose. RESULTS: Institutional expectations regarding the functions of trainees and supervisors were identified, and supervisory relationships appeared entangled within the broader contexts of the training practice, wider profession and political events. Collation of evidence, quality assurance and patient safety were prominent messages within the documents. The institutional hierarchy was accentuated through these messages, and through processes for trainees to raise concerns. Moving down this hierarchy, messages from within the profession changed in emphasis and content. CONCLUSION: With patient safety paramount, and high-quality training and supervision expected, the hierarchical system outlined by the documents is perhaps unsurprising. However, unintended messages may result: collation of evidence may be prized above quality and trainees may feel unable to raise legitimate concerns. Furthermore, conflicting messages from different institutions illustrate the tensions and complexities of GP supervision. For trainees and supervisors, these inconsistencies could lead to different perspectives and expectations as they interact within the supervisory relationship.

Clinical scientists' early career choices and progression: an exploratory mixed methods study.

BACKGROUND: Understanding the influences on healthcare professionals' career choices and progression can inform interventions to improve workforce retention. Retention of health professionals is a high priority worldwide, in order to maintain expertise and meet the needs of national populations. In the UK, investment in clinical scientists' pre-registration education is high and the need to retain motivated scientists recognised. METHODS: We conducted a mixed methods study to investigate the career choices and progression of early career clinical scientists. First job sector and salary of trainees who completed the UK pre-registration Scientist Training Programme (STP) between 2014 and 2019 were analysed using descriptive statistics and Chi-Squared tests. Semi-structured interviews conducted with volunteer practising clinical scientists who completed the programme in 2015 or 2016 were analysed thematically and reviewed for alignment with theories for understanding career choice and workforce retention. RESULTS: Most scientists who completed the STP between 2014 and 2019 obtained a post in the UK National Health Service (NHS) and achieved the expected starting salary. Life scientists were more likely to work in non-NHS healthcare settings than other scientific divisions; and physiological scientists less likely to achieve the expected starting salary. Experiences during training influenced career choice and progression 0-3 years post qualification, as did level of integration of training places with workforce planning. Specialty norms, staff turnover, organisational uncertainty and geographical preferences influenced choices in both the short (0-3 years) and longer term (5 + years). Interviewees reported a strong commitment to public service; and some could foresee that these priorities would influence future decisions about applying for management positions. These factors aligned with the components of job embeddedness theory, particularly that of 'fit'. CONCLUSIONS: Training experiences, personal values, specialty norms and organisational factors all influence UK clinical scientists' early career choices and progression. Job embeddedness theory provides a useful lens through which to explore career choice and progression; and suggests types of intervention that can enhance the careers of this essential group. Interventions need to take account of variations between different scientific specialties.

The Novel Phosphate and Bile Acid Sequestrant Polymer SAR442357 Delays Disease Progression in a Rat Model of Diabetic Nephropathy.

As a gut-restricted, nonabsorbed therapy, polymeric bile acid sequestrants (BAS) play an important role in managing hyperlipidemia and hyperglycemia. Similarly, nonabsorbable sequestrants of dietary phosphate have been used for the management of hyperphosphatemia in end-stage renal disease. To evaluate the potential utility of such polymer sequestrants to treat type 2 diabetes (T2D) and its associated renal and cardiovascular complications, we synthesized a novel polymeric sequestrant, SAR442357, possessing optimized bile acid (BA) and phosphate sequestration characteristics. Long-term treatment of T2D obese cZucker fatty/Spontaneously hypertensive heart failure F1 hybrid (ZSF1) with SAR442357 resulted in enhanced sequestration of BAs and phosphate in the gut, improved glycemic control, lowering of serum cholesterol, and attenuation of diabetic kidney disease (DKD) progression. In comparison, colesevelam, a BAS with poor phosphate binding properties, did not prevent DKD progression, whereas losartan, an angiotensin II receptor blocker that is widely used to treat DKD, showed no effect on hyperglycemia. Analysis of hepatic gene expression levels of the animals treated with SAR442357 revealed upregulation of genes responsible for the biosynthesis of cholesterol and BAs, providing clear evidence of target engagement and mode of action of the new sequestrant. Additional hepatic gene expression pathway changes were indicative of an interruption of the enterohepatic BA cycle. Histopathological analysis of ZSF1 rat kidneys treated with SAR442357 further supported its nephroprotective properties. Collectively, these findings reveal the pharmacological benefit of simultaneous sequestration of BAs and phosphate in treating T2D and its associated comorbidities and cardiovascular complications. SIGNIFICANCE STATEMENT: A new nonabsorbed polymeric sequestrant with optimum phosphate and bile salt sequestration properties was developed as a treatment option for DKD. The new polymeric sequestrant offered combined pharmacological benefits including glucose regulation, lipid lowering, and attenuation of DKD progression in a single therapeutic agent.

Scan multiplier unit for ultrafast laser scanning beyond the inertia limit.

A passive add-on greatly multiplies the sweep rate of any mechanical scanner while also enhancing throughput, enabling a single linear scanner to produce ultrafast 1D or 2D laser scans for general applications.

Single-shot 3D wide-field fluorescence imaging with a Computational Miniature Mesoscope.

Fluorescence microscopes are indispensable to biology and neuroscience. The need for recording in freely behaving animals has further driven the development in miniaturized microscopes (miniscopes). However, conventional microscopes/miniscopes are inherently constrained by their limited space-bandwidth product, shallow depth of field (DOF), and inability to resolve three-dimensional (3D) distributed emitters. Here, we present a Computational Miniature Mesoscope (CM2) that overcomes these bottlenecks and enables single-shot 3D imaging across an 8 mm by 7 mm field of view and 2.5-mm DOF, achieving 7-µm lateral resolution and better than 200-µm axial resolution. The CM2 features a compact lightweight design that integrates a microlens array for imaging and a light-emitting diode array for excitation. Its expanded imaging capability is enabled by computational imaging that augments the optics by algorithms. We experimentally validate the mesoscopic imaging capability on 3D fluorescent samples. We further quantify the effects of scattering and background fluorescence on phantom experiments.

Synchronous Infra-Slow Oscillations Organize Ensembles of Accessory Olfactory Bulb Projection Neurons into Distinct Microcircuits.

The accessory olfactory system controls social and sexual behavior. In the mouse accessory olfactory bulb, the first central stage of information processing along the accessory olfactory pathway, projection neurons (mitral cells) display infra-slow oscillatory discharge with remarkable periodicity. The physiological mechanisms that underlie this default output state, however, remain controversial. Moreover, whether such rhythmic infra-slow activity patterns exist in awake behaving mice and whether such activity reflects the functional organization of the accessory olfactory bulb circuitry remain unclear. Here, we hypothesize that mitral cell ensembles form synchronized microcircuits that subdivide the accessory olfactory bulb into segregated functional clusters. We use a miniature microscope to image the Ca2+ dynamics within the apical dendritic compartments of large mitral cell ensembles in vivo We show that infra-slow periodic patterns of concerted neural activity, indeed, reflect the idle state of accessory olfactory bulb output in awake male and female mice. Ca2+ activity profiles are distinct and glomerulus-specific. Confocal time-lapse imaging in acute slices reveals that groups of mitral cells assemble into microcircuits that exhibit correlated Ca2+ signals. Moreover, electrophysiological profiling of synaptic connectivity indicates functional coupling between mitral cells. Our results suggest that both intrinsically rhythmogenic neurons and neurons entrained by fast synaptic drive are key elements in organizing the accessory olfactory bulb into functional microcircuits, each characterized by a distinct default pattern of infra-slow rhythmicity.SIGNIFICANCE STATEMENT Information processing in the accessory olfactory bulb (AOB) plays a central role in conspecific chemosensory communication. Surprisingly, many basic physiological principles that underlie neuronal signaling in the AOB remain elusive. Here, we show that AOB projection neurons (mitral cells) form parallel synchronized ensembles both in vitro and in vivo Infra-slow synchronous oscillatory activity within AOB microcircuits thus adds a new dimension to chemosensory coding along the accessory olfactory pathway.

Simultaneous multiplane imaging with reverberation two-photon microscopy.

Multiphoton microscopy has gained enormous popularity because of its unique capacity to provide high-resolution images from deep within scattering tissue. Here, we demonstrate video-rate multiplane imaging with two-photon microscopy by performing near-instantaneous axial scanning while maintaining three-dimensional micrometer-scale resolution. Our technique, termed reverberation microscopy, enables the monitoring of neuronal populations over large depth ranges and can be implemented as a simple add-on to a conventional design.

A Lack of Communication and Awareness in Nontechnical Skills Training? A Qualitative Analysis of the Perceptions of Trainers and Trainees in Surgical Training.

OBJECTIVE: To examine the perceptions of surgical trainees and trainers towards nontechnical skills (NTS) as a concept, its role in training, and the challenges of developing these skills. DESIGN: A case series of semistructured interviews using an interpretivist grounded theory approach for qualitative analysis. SETTING: East Midlands (North) core surgical training programme in the United Kingdom. PARTICIPANTS: Ten out of 81 volunteer core surgical trainees and academic educational supervisors (consultant surgeon trainers). RESULTS: Understanding of NTS was consistent amongst trainers and trainees but the conceived definition of NTS was much broader than previous definitions. Most viewed NTS as important for surgeons. Trainees believed trainers did not appreciate or were unaware of NTS, likely because of a lack of discussion in practice. Trainers had several reasons for not discussing NTS including insufficient personal relationships with trainees and a lack of robust evidence on which to base discussions. A lack of insight into NTS and surgeon arrogance were suggested as barrier to effective learning. CONCLUSIONS: Apparent discordant perceptions may be contributing to a lack of focused NTS feedback for surgeons in training. To implement NTS training changes, more will have to be done to develop a shared understanding.

Factors affecting recruitment into General Practice: a double binary choice approach.

Recruitment to General Practice (GP) is currently low in many countries. Here we focus on two binary choices for junior doctors: first, whether to apply to GP; second, whether to accept a GP training place if offered. Previous attitudinal studies have indicated factors claimed to affect recruitment. The current study goes further by quantifying the relative impact of different factors on the propensity of candidates to apply to GP and accept a training place. An online questionnaire was sent to candidates applying to United Kingdom (UK) specialty training in 2015. Descriptive statistics and a path analysis evaluated the importance of various factors on GP applications. Our results were synthesised with an analysis of data from the online applications portal. With 3838 candidates responding to the survey, the path analysis showed that personality and previous GP experiences were strongly associated with the decision to apply. There was some evidence that it was easier to enter GP than other specialties; in terms of deciding whether to accept, the evidence suggests GP was a backup plan for around 9% of candidates who accepted a GP post. Our results indicate that recruitment initiatives should focus on candidates who apply to GP but not as first choice or consider GP but do not apply, particularly by providing substantial experience of GP and accentuating the positives of the specialty such as work-life balance and the intellectual challenge of working with patients in primary care. Acceptance of a GP place may also depend on competition for places in other specialties.